Scen-scada security: an enhanced osprey optimization-based cyber attack detection model in supervisory control and data acquisition system using serial cascaded ensemble network

A significant role of Supervisory Control and Data Acquisition (SCADA) systems is supporting power system operation, where Information and Communication Technology (ICT) is adopted to interconnect the devices, and it increases system complexity. Because of the interconnection of SCADA systems, the complexity is increased, and there is a chance of cyber security vulnerabilities. In addition, the SCADA networks with legacy devices are affected by inbuilt cyber security deliberation that has provided severe cyber security vulnerable points. With the adoption of local-area networks and Internet Protocol (IP)-driven proprietary, malicious or unauthorized user accesses the information from outside sources, and hence, the SCADA systems are weakened by the elaborate attacks. SCADA systems need to deliberate the Denial of Service (DoS) and catastrophic failure as well as maloperation, which may subsequently compromise the stability and safety of the operations in the power system. Therefore, the pertinent priority in SCADA is to strengthen cyber security for guaranteeing reliable operation and also, the system stability is governed with respect to communications integrity. The smart grid features are used in the conventional Machine learning approaches for identifying cyber-attacks. Hence, implementing an efficient and accurate cyber-attack detection approach with less computational overhead is still a crucial research problem in SCADA. So, a novel and secure model for cyber-attack detection in the SCADA system using advanced deep learning techniques together with the heuristic algorithm is executed in this research work. The SCADA data are collected from various power grids. The features from these data are optimally selected and fused with the optimal weights in order to obtain the weighted optimal features. The weighted optimal feature selection is done with the aid of the Enhanced Osprey Optimization Algorithm (EOOA). These optimally selected weighted features are given to the Serial Cascaded Ensemble Network (SCEN) to obtain the final detection output. The developed SCEN is made with the cascading of Autoencoder, Dilated Bidirectional Long Short Term Memory (Bi-LSTM), and Bayesian classifier. The parameters in the SCEN are tuned using the executed IOOA. The final detection of the presence or absence of a cyber attack is evaluated by this SCEN. The performance and the effectiveness of the developed model are verified and contrasted by conducting various experiments.